scholarly journals Large-Scale Linear RankSVM

2014 ◽  
Vol 26 (4) ◽  
pp. 781-817 ◽  
Author(s):  
Ching-Pei Lee ◽  
Chih-Jen Lin
Keyword(s):  
Decision Trees ◽  
Computational Efficiency ◽  
Efficient Algorithm ◽  
Large Scale ◽  
Learning To Rank ◽  
Gradient Boosting ◽  
Baseline Model ◽  
Nonlinear Methods ◽  
Advantages And Disadvantages ◽  
Linear Ranksvm

Linear rankSVM is one of the widely used methods for learning to rank. Although its performance may be inferior to nonlinear methods such as kernel rankSVM and gradient boosting decision trees, linear rankSVM is useful to quickly produce a baseline model. Furthermore, following its recent development for classification, linear rankSVM may give competitive performance for large and sparse data. A great deal of works have studied linear rankSVM. The focus is on the computational efficiency when the number of preference pairs is large. In this letter, we systematically study existing works, discuss their advantages and disadvantages, and propose an efficient algorithm. We discuss different implementation issues and extensions with detailed experiments. Finally, we develop a robust linear rankSVM tool for public use.

scholarly journals Machine learning techniques for short-term solar power stations operational mode planning

2018 ◽  
Vol 51 ◽  
pp. 02004 ◽  
Author(s):  
Stanislav Eroshenko ◽  
Alexandra Khalyasmaa ◽  
Denis Snegirev
Keyword(s):  
Decision Trees ◽  
Solar Power ◽  
Machine Learning Techniques ◽  
Gradient Boosting ◽  
Operational Mode ◽  
Mathematical Methods ◽  
Short Term ◽  
Advantages And Disadvantages ◽  
Power Stations ◽  
Operational Forecasting

The paper presents the operational model of very-short term solar power stations (SPS) generation forecasting developed by the authors, based on weather information and built into the existing software product as a separate module for SPS operational forecasting. It was revealed that one of the optimal mathematical methods for SPS generation operational forecasting is gradient boosting on decision trees. The paper describes the basic principles of operational forecasting based on the boosting of decision trees, the main advantages and disadvantages of implementing this algorithm. Moreover, this paper presents an example of this algorithm implementation being analyzed using the example of data analysis and forecasting the generation of the existing SPS.

scholarly journals Machine learning techniques for short-term solar power stations operational mode planning

2018 ◽  
Vol 51 ◽  
pp. 02004
Author(s):  
Stanislav Eroshenko ◽  
Alexandra Khalyasmaa ◽  
Denis Snegirev
Keyword(s):  
Decision Trees ◽  
Solar Power ◽  
Machine Learning Techniques ◽  
Gradient Boosting ◽  
Operational Mode ◽  
Mathematical Methods ◽  
Short Term ◽  
Advantages And Disadvantages ◽  
Power Stations ◽  
Operational Forecasting

The paper presents the operational model of very-short term solar power stations (SPS) generation forecasting developed by the authors, based on weather information and built into the existing software product as a separate module for SPS operational forecasting. It was revealed that one of the optimal mathematical methods for SPS generation operational forecasting is gradient boosting on decision trees. The paper describes the basic principles of operational forecasting based on the boosting of decision trees, the main advantages and disadvantages of implementing this algorithm. Moreover, this paper presents an example of this algorithm implementation being analyzed using the example of data analysis and forecasting the generation of the existing SPS.

Implantable neural interfaces

2018 ◽  
Author(s):  
Stefano Vassanelli
Keyword(s):  
Brain Function ◽  
Large Scale ◽  
Ionic Channels ◽  
Patch Clamp Technique ◽  
Advantages And Disadvantages ◽  
Optogenetic Stimulation ◽  
Physical Interfaces ◽  
The Brain

Establishing direct communication with the brain through physical interfaces is a fundamental strategy to investigate brain function. Starting with the patch-clamp technique in the seventies, neuroscience has moved from detailed characterization of ionic channels to the analysis of single neurons and, more recently, microcircuits in brain neuronal networks. Development of new biohybrid probes with electrodes for recording and stimulating neurons in the living animal is a natural consequence of this trend. The recent introduction of optogenetic stimulation and advanced high-resolution large-scale electrical recording approaches demonstrates this need. Brain implants for real-time neurophysiology are also opening new avenues for neuroprosthetics to restore brain function after injury or in neurological disorders. This chapter provides an overview on existing and emergent neurophysiology technologies with particular focus on those intended to interface neuronal microcircuits in vivo. Chemical, electrical, and optogenetic-based interfaces are presented, with an analysis of advantages and disadvantages of the different technical approaches.

scholarly journals Why ability point estimates can be pointless: a primer on using skill measures from large-scale assessments in secondary analyses

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Clemens M. Lechner ◽  
Nivedita Bhaktha ◽  
Katharina Groskurth ◽  
Matthias Bluemke
Keyword(s):  
Measurement Error ◽  
Statistical Models ◽  
Test Scores ◽  
Large Scale ◽  
Equation Modeling ◽  
Model Parameters ◽  
Advantages And Disadvantages ◽  
Point Estimates ◽  
Secondary Analyses ◽  
Large Scale Assessments

AbstractMeasures of cognitive or socio-emotional skills from large-scale assessments surveys (LSAS) are often based on advanced statistical models and scoring techniques unfamiliar to applied researchers. Consequently, applied researchers working with data from LSAS may be uncertain about the assumptions and computational details of these statistical models and scoring techniques and about how to best incorporate the resulting skill measures in secondary analyses. The present paper is intended as a primer for applied researchers. After a brief introduction to the key properties of skill assessments, we give an overview over the three principal methods with which secondary analysts can incorporate skill measures from LSAS in their analyses: (1) as test scores (i.e., point estimates of individual ability), (2) through structural equation modeling (SEM), and (3) in the form of plausible values (PVs). We discuss the advantages and disadvantages of each method based on three criteria: fallibility (i.e., control for measurement error and unbiasedness), usability (i.e., ease of use in secondary analyses), and immutability (i.e., consistency of test scores, PVs, or measurement model parameters across different analyses and analysts). We show that although none of the methods are optimal under all criteria, methods that result in a single point estimate of each respondent’s ability (i.e., all types of “test scores”) are rarely optimal for research purposes. Instead, approaches that avoid or correct for measurement error—especially PV methodology—stand out as the method of choice. We conclude with practical recommendations for secondary analysts and data-producing organizations.

scholarly journals SARS-CoV-2 vaccines strategies: a comprehensive review of phase 3 candidates

npj Vaccines ◽  
2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Nikolaos C. Kyriakidis ◽  
Andrés López-Cortés ◽  
Eduardo Vásconez González ◽  
Alejandra Barreto Grimaldos ◽  
Esteban Ortiz Prado
Keyword(s):  
Neutralizing Antibodies ◽  
Large Scale ◽  
Vaccine Development ◽  
Rna Virus ◽  
Protein S ◽  
Effective Vaccine ◽  
Phase 3 ◽  
Vaccine Candidates ◽  
Advantages And Disadvantages ◽  
The World

AbstractThe new SARS-CoV-2 virus is an RNA virus that belongs to the Coronaviridae family and causes COVID-19 disease. The newly sequenced virus appears to originate in China and rapidly spread throughout the world, becoming a pandemic that, until January 5th, 2021, has caused more than 1,866,000 deaths. Hence, laboratories worldwide are developing an effective vaccine against this disease, which will be essential to reduce morbidity and mortality. Currently, there more than 64 vaccine candidates, most of them aiming to induce neutralizing antibodies against the spike protein (S). These antibodies will prevent uptake through the human ACE-2 receptor, thereby limiting viral entrance. Different vaccine platforms are being used for vaccine development, each one presenting several advantages and disadvantages. Thus far, thirteen vaccine candidates are being tested in Phase 3 clinical trials; therefore, it is closer to receiving approval or authorization for large-scale immunizations.

scholarly journals Renormalization group theory of molecular dynamics

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Daiji Ichishima ◽  
Yuya Matsumura
Keyword(s):  
Molecular Dynamics ◽  
Renormalization Group ◽  
Critical Exponent ◽  
Computational Efficiency ◽  
Large Scale ◽  
Dissipative Particle Dynamics ◽  
Computational Cost ◽  
Coarse Graining ◽  
Spatial Dimension ◽  
Deborah Number

AbstractLarge scale computation by molecular dynamics (MD) method is often challenging or even impractical due to its computational cost, in spite of its wide applications in a variety of fields. Although the recent advancement in parallel computing and introduction of coarse-graining methods have enabled large scale calculations, macroscopic analyses are still not realizable. Here, we present renormalized molecular dynamics (RMD), a renormalization group of MD in thermal equilibrium derived by using the Migdal–Kadanoff approximation. The RMD method improves the computational efficiency drastically while retaining the advantage of MD. The computational efficiency is improved by a factor of $$2^{n(D+1)}$$ 2 n ( D + 1 ) over conventional MD where D is the spatial dimension and n is the number of applied renormalization transforms. We verify RMD by conducting two simulations; melting of an aluminum slab and collision of aluminum spheres. Both problems show that the expectation values of physical quantities are in good agreement after the renormalization, whereas the consumption time is reduced as expected. To observe behavior of RMD near the critical point, the critical exponent of the Lennard-Jones potential is extracted by calculating specific heat on the mesoscale. The critical exponent is obtained as $$\nu =0.63\pm 0.01$$ ν = 0.63 ± 0.01 . In addition, the renormalization group of dissipative particle dynamics (DPD) is derived. Renormalized DPD is equivalent to RMD in isothermal systems under the condition such that Deborah number $$De\ll 1$$ D e ≪ 1 .

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